{"id":"W4402050532","doi":"10.1093/pnasnexus/pgae360","title":"Prioritizing social vulnerability in urban heat mitigation","year":2024,"lang":"en","type":"article","venue":"PNAS Nexus","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Department of Mechanical Engineering, University of Texas at Austin; Advanced Scientific Computing Research; Nuclear Safety and Security Commission; National Aeronautics and Space Administration; U.S. Department of Energy; Earth Sciences Division; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Vulnerability (computing); Social vulnerability; Urban heat island; Environmental planning; Environmental science; Geography; Computer science; Psychology; Computer security; Meteorology; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002728766,0.00008891115,0.00008719158,0.0000352427,0.00008849833,0.00006089553,0.00007273518,0.00005366129,0.0003831429],"category_scores_gemma":[0.00003672059,0.00008886017,0.00004026347,0.0002898298,0.00008025968,0.0003079952,0.00004730943,0.0001626664,0.0004993856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004096627,"about_ca_system_score_gemma":0.0000148171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00058446,"about_ca_topic_score_gemma":0.0003594882,"domain_scores_codex":[0.9990668,0.00006890365,0.0001731058,0.0002869419,0.000195054,0.0002091409],"domain_scores_gemma":[0.9997856,0.00006044229,0.000008883496,0.00009888355,0.000003033683,0.00004317657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003785916,0.0002441808,0.6448662,0.0002053924,0.00002208967,0.0001442619,0.02953799,0.0004579613,0.1947726,0.007095812,0.04432395,0.0782918],"study_design_scores_gemma":[0.0003857344,0.00004791628,0.9332511,0.00006550568,0.00001702412,0.00001418867,0.0002059106,0.013245,0.004577741,0.01908886,0.02875162,0.000349446],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9758449,0.0001535783,0.0001665924,0.000791761,0.0002925791,0.0001876023,0.000005975412,0.0001100794,0.02244692],"genre_scores_gemma":[0.9989507,0.000002430981,0.0001647343,0.0001086679,0.0001885252,0.0000263881,0.00001528865,0.00001189012,0.0005313515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2883849,"threshold_uncertainty_score":0.6418759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01231730705426209,"score_gpt":0.2555614298666995,"score_spread":0.2432441228124374,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}